Grid Resilience and Energy Storage Leveraging Machine Learning for Grid Services and Ancillary

Zhengjie Yang

doi:10.62051/ijcsit.v3n2.27

Authors

Zhengjie Yang

DOI:

https://doi.org/10.62051/ijcsit.v3n2.27

Keywords:

Machine learning, Energy storage, Market optimization, Economic efficiency

Abstract

This paper discusses the challenges and opportunities of optimizing the economic benefits of energy storage systems in the electricity market, focusing on the key role of machine learning in energy storage bidding optimization. By analyzing complex market data and applying different machine learning models, such as regression models, time series prediction models, and deep learning models, energy storage systems are able to accurately predict market price fluctuations, optimize charge and discharge strategies, and maximize their economic returns in the energy market, ancillary services market, and capacity market. Applying these technologies not only improves the system's market participation ability but also significantly improves the stability and efficiency of the power system.

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